Methods and systems for social networking with autonomous mobile agents

ABSTRACT

Exemplary methods and systems are presented for social networking applications using autonomous mobile agents. Communication links are established based on geographic proximity and distance described as a domain in which resident agents are detected and identified. The communication links thus established allow platform independent communication along communication channels that are dynamically derived. Incorporation of computer machines and feature data sets (including attractiveness data) permit agent classification and inter-agent notification of classification results.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of patent application Ser. No.16/186,149, filed on 9 Nov. 2018, and patent application Ser. No.14/217,173, filed on 17 Mar. 2014, which claims the benefit ofProvisional Application Ser. No. 61/800,009, filed on 15 Mar. 2013, thecontents of which are herein incorporated by reference in theirentirety.

BACKGROUND OF THE DISCLOSURE 1. Field of the Disclosure

The disclosure relates generally to social networking methods andsystems and specifically in certain embodiments to methods and systemsfor proximity-driven social networking applications implemented withautonomous mobile agents incorporating data mining and machine learningclassification.

2. General Background

Mobile device users (agents) provide a platform for a social networkinginteraction that is motivated by groupings of proximate users,independent of other typical social network connections. Geographicalproximity, and additional features proximity, provides categorizationswhich establish a set of proximate agents which are thus associated forinteraction.

The information exchange between any two agents may be performed withopen identity, or with partial or complete anonymity

Types of data exchanged may be user-driven communication, user-definedauto-categorizations, and automatic machine classification operating onfeature data inputs from associated agents.

Data mining and machine learning technologies may be used to developautomatic computer constructs for agents whom can operate on featuredata sets from associated neighbor agents as input. Computer machineconstructs within an agent perform classification operations on featuredata sets from associated agents and the classification results may becommunicated back to associated agents. This information exchange may beautonomously derived.

As an example, facial recognition technology (FRT) provides a means toderive feature data to describe the facial appearance of a user agent.Machine learning provides a technology by which the facial attractionpreferences of an associated (proximate group) agent may be modeled. Thecomputer machine of the associated agent may operate on the feature dataset from the parent agent and thus classify the associated agent asattracted, or not attracted to the parent agent. This process allows theassociated agents to be thusly classified. Classification using facialrecognition technology (FRT) and machine learning is one example. Otherclassifications may be implemented into this structure.

It is desirable to address the limitations in the art, e.g., to applymethods and systems for proximity-driven social networking applicationsimplemented with autonomous mobile agents incorporating data mining andmachine learning classification.

BRIEF DESCRIPTION OF THE DRAWINGS

By way of example, reference will now be made to the accompanyingdrawings, which are not to scale.

FIG. 1A illustrates an exemplary networked environment and its relevantcomponents according to certain embodiments.

FIG. 1B is an exemplary block diagram of a computing device that may beused to implement certain embodiments.

FIG. 2 is an exemplary block diagram of a network that may be used toimplement certain embodiments.

FIG. 3 illustrates an exemplary environment of agents in which theproximate group (neighbor group) is shown within the discriminationdomain.

FIG. 4 illustrates an implementation in which the proximate zone isestablished with locally provided log-on key.

FIG. 5 illustrates an implementation in which the proximate zone isestablished with a log-on key provided at more than one location.

FIG. 6 illustrates and exemplary environment of agents created byjoining two remote proximate zones.

FIG. 7 illustrates the inter-agent communication in a proximatesubgroup, independent of any classification operation.

FIG. 8 illustrates the inter-agent communication for the classificationoperation in an exemplary environment proximate subgroup of agents inwhich the proximate group (neighbor group) has been classified.

FIG. 9 is a schematic diagram of a prototypical agent structure asimplemented herein.

FIG. 10 is a diagram of an agent layer structure for an example binaryclassification.

FIG. 11 is a diagram of an agent layer structure for an example binarymultiplex classification.

FIG. 12 is a diagram of an agent layer structure for an examplemultivariate classification.

FIG. 13 depicts the agent initialization process.

FIG. 14 depicts the network discrimination process to determine aproximate group.

FIG. 15 shows an exemplary process in which parent agent feature data isclassified by an associated neighbor agent.

FIG. 16 shows the use of additional computer resources by the computermachine of an agent.

FIG. 17 shows the use of a proximity zone to request data from a targetlocation.

FIG. 18 is an exemplary block diagram of a process to detect andidentify a proximate group of agents and establish communication, andclassification amongst the agents.

FIG. 19A is a detailed exemplary block diagram of the first portion ofthe processes depicted in FIG. 18.

FIG. 19B is a detailed exemplary block diagram of the second portion ofthe processes depicted in FIG. 18.

FIG. 19C is a detailed exemplary block diagram of the third portion ofthe processes depicted in FIG. 18.

DETAILED DESCRIPTION

Those of ordinary skill in the art will realize that the followingdescription of the present invention is illustrative only and not in anyway limiting. Other embodiments of the invention will readily suggestthemselves to such skilled persons, having the benefit of thisdisclosure. Reference will now be made in detail to specificimplementations of the present invention as illustrated in theaccompanying drawings. The same reference numbers will be usedthroughout the drawings and the following description to refer to thesame or like parts.

Further, certain figures in this specification are flow chartsillustrating methods and systems. It will be understood that each blockof these flow charts, and combinations of blocks in these flow charts,may be implemented by computer program instructions. These computerprogram instructions may be loaded onto a computer or other programmableapparatus to produce a machine, such that the instructions which executeon the computer or other programmable apparatus create structures forimplementing the functions specified in the flow chart block or blocks.These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable apparatus to function in a particular manner, such that theinstructions stored in the computer-readable memory produce an articleof manufacture including instruction structures which implement thefunction specified in the flow chart block or blocks. The computerprogram instructions may also be loaded onto a computer or otherprogrammable apparatus to cause a series of operational steps to beperformed on the computer or other programmable apparatus to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide steps forimplementing the functions specified in the flow chart block or blocks.

Accordingly, blocks of the flow charts support combinations ofstructures for performing the specified functions and combinations ofsteps for performing the specified functions. It will also be understoodthat each block of the flow charts, and combinations of blocks in theflow charts, can be implemented by special purpose hardware-basedcomputer systems which perform the specified functions or steps, orcombinations of special purpose hardware and computer instructions.

For example, any number of computer programming languages, such as C,C++, C # (CSharp), Perl, Ada, Python, Pascal, SmallTalk, FORTRAN,assembly language, and the like, may be used to implement aspects of thepresent invention. Further, various programming approaches such asprocedural, object-oriented or artificial intelligence techniques may beemployed, depending on the requirements of each particularimplementation. Compiler programs and/or virtual machine programsexecuted by computer systems generally translate higher levelprogramming languages to generate sets of machine instructions that maybe executed by one or more processors to perform a programmed functionor set of functions.

The term “machine-readable medium” should be understood to include anystructure that participates in providing data which may be read by anelement of a computer system. Such a medium may take many forms,including but not limited to, non-volatile media, volatile media, andtransmission media. Non-volatile media include, for example, optical ormagnetic disks and other persistent memory. Volatile media includedynamic random access memory (DRAM) and/or static random access memory(SRAM). Transmission media include cables, wires, and fibers, includingthe wires that comprise a system bus coupled to processor. Common formsof machine-readable media include, for example, a floppy disk, aflexible disk, a hard disk, a magnetic tape, any other magnetic medium,a CD-ROM, a DVD, any other optical medium.

FIG. 1A depicts an exemplary networked environment 100 in which systemsand methods, consistent with exemplary embodiments, may be implemented.As illustrated, networked environment 100 may include a content server105, a receiver 115, and a network 110. The exemplary simplified numberof content servers 105, receivers 115, and networks 110 illustrated inFIG. 1A can be modified as appropriate in a particular implementation.In practice, there may be additional content servers 105, receivers 115,and/or networks 110.

In certain embodiments, a receiver 115 may include any suitable form ofmultimedia playback device, including, without limitation, a cable orsatellite television set-top box, a DVD player, a digital video recorder(DVR), or a digital audio/video stream receiver, decoder, and player. Areceiver 115 may connect to network 110 via wired and/or wirelessconnections, and thereby communicate or become coupled with contentserver 105, either directly or indirectly. Alternatively, receiver 115may be associated with content server 105 through any suitable tangiblecomputer-readable media or data storage device (such as a disk drive,CD-ROM, DVD, or the like), data stream, file, or communication channel.

Network 110 may include one or more networks of any type, including aPublic Land Mobile Network (PLMN), a telephone network (e.g., a PublicSwitched Telephone Network (PSTN) and/or a wireless network), a localarea network (LAN), a metropolitan area network (MAN), a wide areanetwork (WAN), an Internet Protocol Multimedia Subsystem (IMS) network,a private network, the Internet, an intranet, and/or another type ofsuitable network, depending on the requirements of each particularimplementation.

One or more components of networked environment 100 may perform one ormore of the tasks described as being performed by one or more othercomponents of networked environment 100.

FIG. 1B is an exemplary diagram of a computing device 150 that may beused to implement aspects of certain embodiments of the presentinvention, such as aspects of content server 105 or of receiver 115.Computing device 150 may include a bus 190, one or more processors 175,a main memory 170, a read-only memory (ROM) 180, a storage device 185,one or more input devices 155, one or more output devices 160, and acommunication interface 165. Bus 190 may include one or more conductorsthat permit communication among the components of computing device 150.

Processor 175 may include any type of conventional processor,microprocessor, or processing logic that interprets and executesinstructions. Main memory 170 may include a random-access memory (RAM)or another type of dynamic storage device that stores information andinstructions for execution by processor 175. ROM 180 may include aconventional ROM device or another type of static storage device thatstores static information and instructions for use by processor 175.Storage device 185 may include a magnetic and/or optical recordingmedium and its corresponding drive.

Input device(s) 155 may include one or more conventional mechanisms thatpermit a user to input information to computing device 150, such as akeyboard, a mouse, a pen, a stylus, handwriting recognition, voicerecognition, biometric mechanisms, and the like. Output device(s) 160may include one or more conventional mechanisms that output informationto the user, including a display, a projector, an A/V receiver, aprinter, a speaker, and the like. Communication interface 165 mayinclude any transceiver-like mechanism that enables computingdevice/server 150 to communicate with other devices and/or systems. Forexample, communication interface 165 may include mechanisms forcommunicating with another device or system via a network, such asnetwork 110 as shown in FIG. 1A.

As will be described in detail below, computing device 150 may performoperations based on software instructions that may be read into memory170 from another computer-readable medium, such as data storage device185, or from another device via communication interface 165. Thesoftware instructions contained in memory 170 cause processor, 175, toperform processes that will be described later. Alternatively, hardwiredcircuitry may be used in place of or in combination with softwareinstructions to implement processes consistent with the presentinvention. Thus, various implementations are not limited to any specificcombination of hardware circuitry and software.

A web browser comprising a web browser user interface may be used todisplay information (such as textual and graphical information) on thecomputing device 150. The web browser may comprise any type of visualdisplay capable of displaying information received via the network 110shown in FIG. 1A, such as Microsoft's Internet Explorer browser,Netscape's Navigator browser, Mozilla's Firefox browser, PalmSource'sWeb Browser, Google's Chrome browser or any other commercially availableor customized browsing or other application software capable ofcommunicating with network 110. The computing device 150 may alsoinclude a browser assistant. The browser assistant may include aplug-in, an applet, a dynamic link library (DLL), or a similarexecutable object or process. Further, the browser assistant may be atoolbar, software button, or menu that provides an extension to the webbrowser. Alternatively, the browser assistant may be a part of the webbrowser, in which case the browser would implement the functionality ofthe browser assistant.

The browser and/or the browser assistant may act as an intermediarybetween the user and the computing device 150 and/or the network 110.For example, source data or other information received from devicesconnected to the network 110 may be output via the browser. Also, boththe browser and the browser assistant are capable of performingoperations on the received source information prior to outputting thesource information. Further, the browser and/or the browser assistantmay receive user input and transmit the inputted data to devicesconnected to network 110.

Similarly, certain embodiments of the present invention described hereinare discussed in the context of the global data communication networkcommonly referred to as the Internet. Those skilled in the art willrealize that embodiments of the present invention may use any othersuitable data communication network, including without limitation directpoint-to-point data communication systems, dial-up networks, personal orcorporate Intranets, proprietary networks, or combinations of any ofthese with or without connections to the Internet.

The building blocks of a system according to certain embodiments consistof a universe of agents (typically embodied as mobile users), aproximity discrimination module, a classification module, andnotification interface. The structure and implementation of the systemin certain embodiments, and of each of its building blocks, aredescribed below and depicted in the accompanying figures.

FIG. 2 depicts an exemplary networked environment 200 in which systemsand methods, consistent with exemplary embodiments, may be implemented.As illustrated, networked environment, 200, will include a proximitydiscrimination module (discrimination module), 220, and a classificationmodule, 230. The parent agent, 205, initiates a query in which thediscrimination module, 220, discriminates the group of all agents, 215,to a proximate group, 225. The discrimination parameters may be refinedin an iterative process, 240. The classification module, 230, furtheroperates on the proximate group to yield the classified proximate group,235. An abbreviated structure may be utilized in which classification isnot performed, 245.

The group of all agents, 215, consists of all members participating inthe system. Members of the system may consist of, but are not limitedto, users with cell phones, tablets, desktops, and shared environmentcomputational devices.

The parent agent, 205, is a subgroup of all agents which typicallyconsists of a single member. An agent may be designated as parent agentby specifying a set of parameters for proximity discrimination. Inimplementations where a single agent functions as parent agent, one ormore agents, or all agents, may independently serve as the parent agent.

The functional concept according to certain embodiments is that aneighborhood of users can be established about some location.Communication between the users in the proximate subgroup may beestablished. Additional operations (classifications) may be performedand communicated openly or anonymously so that the parent (or other)agent knows some characteristic of the associated neighbor agents. Forexample, the parent agent may know that there are 20 Republican in theproximate group and 10 others. Relative geographic data may be includedin the inter-agent exchange to be communicated to an agent. For example,the parent agent may see that of the 30 agents in the room, 20 of themare Republicans, and they are all at the front of the room.

Referring back to the exemplary embodiment depicted in FIG. 2, thediscrimination module, 220, consists of a system by which the proximatesubgroup, 225, is determined.

The proximate group, 225 consists of the subgroup of all users that arewithin specified proximity to geographical position data and/orproximity input parameters specified by the parent agent, 205.

The proximate group, 225, is the group of agents amongst whichinter-agent communication may occur.

FIG. 3 depicts an exemplary proximate group of associated agentsconsisting of the parent agent, 205, and associated neighbor agents,210, that are all within the discrimination zone, 250, and excludedagents, 255, that are not part of the associated neighbor group.

Note that for each of the agents that functions as a parent agent, theresultant proximate group may be different.

It may be possible to specify parameters such that the discriminationzone does not contain the parent agent.

Proximity parameters may be of any qualitative nature. Examples of suchproximity parameters include, but are not limited to, hair color, agerange, height range, political affiliation, and the like.

Proximity parameters may be geographical, e.g., at (or within 0.25 mileof) latitude/longitude coordinates 34.8940, −117.0472. Other parametersmay be used, depending on the requirements of each particularimplementation. For example, the proximity parameters may specifyteenage males within the boundaries of Disneyland, Calif. As anotherexample, the proximity parameters may specify a domain that is within 50feet of the centerline of Colorado Blvd, in Pasadena, Calif., during theperiod from 8:00 AM to 11:00 AM on Jan. 1, 2016.

The parameters may be specified which create a collection of domainswhich are not contiguous. For example, the proximity parameters mayspecify the collection of domains defined as within 100 feet of any ofthe In-N-Out® Burger restaurants in Long Beach, Calif.

The discrimination operation may be parametric only, utilizing agentinputs.

In one exemplary implementation, the discrimination operation mayperform in whole or in part using agent position data. For example,individual agents, or groups of agents may be added to or removed fromthe proximate group using a map display depicting the relative positionof agents.

In certain embodiments, the discrimination module, 220, may consist of aremote server which receives geographic position data and/or additionalproximity input parameters from all agents, 215, to discriminate thesubgroup of all agents that are within specified proximity togeographical position data and/or proximity input parameters of theparent agent, 205.

In certain embodiments, the discrimination module, 220, may consist of aset of operations that are performed on one or more agents withoutcommunication with a remote device to perform the discriminationoperation.

In certain embodiments, the discrimination module, 220, may consist, inpart, of a proximity key, e.g., a log-on displayed at a bar, tofacilitate an associative collection of participating agents at thatlocation. This is depicted in FIG. 4, in which a proximate group ofneighbor agents, 210, are established at some locality, 300, using thelocally provided log-on key, 305. Agents that do not apply the log-onkey comprise the group of excluded agents, 255.

The element that provides the log-on key, 305, may serve as a parentagent, or may simply be a mechanism to provide a log-on processinitiated by an agent within the local group, or by an agent external tothe local group.

A log-on key may be provided at different locations to establish aproximate group consisting of geographically remote subgroups, asdepicted in FIG. 5, which shows two remote localities, 301 and 302, inwhich a log-on key, 305, is provided at more than one location via acommunication link, 350, to establish a proximate group of neighboragents, 210. Agents that do not apply the log-on key comprise the groupof excluded agents, 255.

Referring back to exemplary diagram in FIG. 2, upon development of aproximate group, 225, the proximity parameters may be modified, 240,whereupon the proximate group is re-evaluated. This process may berepeated and adjusted any number of times.

Remote proximate groups may be joined into one proximate group comprisedof remote groups of associated agents, as shown in FIG. 6, which showsthe proximate group within the discrimination zone, 250, composed of aparent agent, 205 and associated neighbor agents, 210, and the proximategroup within the discrimination zone, 251, composed of a parent agent,206 and associated neighbor agents, 211, joined, 400, to form oneeffective associated group

The joined group thus formed may have either parent agent, 205, or 206,assume the role as the joined group parent agent.

A proximate group may have more than one parent agent.

The role of parent agent may be switched amongst agents, or sharedamongst agents.

A proximate group of associated agents may be broken into more than onesmaller disjoint proximate groups.

The grouping thus established may be temporary, with associationsexpiring in a fixed time, or expiring after there has been a lapse ofinter-agent communication for a fixed time.

The groupings thus established may be saved, e.g., the group that was atthe bar on Friday night.

The proximity group may be geographically transient, in which the groupconsists of agents that are within a certain geographical proximity to alocation, e.g., the parent agent's location, which may be moving. As anexample, consider agents in cars on the highway, wherein the proximitygroup consists of agents that are sufficiently near to the cartransporting the parent agent. In essence, the discrimination zone, 250,as shown in FIG. 3 moves with a moving parent agent.

In another exemplary implementation, the proximity parameters may bespecified such that the proximity region does not follow a parent agent,but does move. As an example, a zone may move with a float in a parade,even though no parent agent is moving with the float.

In one embodiment, communication and notification may commence, andcontinue between agents in the proximate group, 225, with our withoutclassification. This is shown in FIG. 7, in which the parent agent, 205,and associated neighbor agents, 210, within the discrimination zone,250, communicate, 450. Agents, 255, outside of the discrimination zoneare excluded from the communication.

In one implementation, each neighbor agent communicates only with theparent agent (restricted communication).

In one implementation, all agents may communicate to each other agent inthe proximate group (open communication).

In one implementation, communication permissions may be different fordifferent agents (structured communication).

In one implementation the communication structure may be initialized,but is not locked such that the communication structure may emerge basedon agent level decisions (free communication)

The inter-agent communication may include notification to the parentagent of the existence of each neighbor agent (anonymous notification).

The communication to the parent agent may also include relativepositional data, and possible additional neighbor agent data(non-anonymous notification).

The inter-agent communication may include notification to the neighboragent that the neighbor agent has been discriminated into a proximategroup (anonymous notification).

The communication to the neighbor agent may also include relativepositional data, and possible additional neighbor agent data(non-anonymous notification).

The inter-agent communication structure may be configured such thatcombinations of one-way, two-way, anonymous, and non-anonymouscommunication are used within an agent group.

The communication may also include any combination of directedcommunications, not limited to, text messages, emails, file transfers,and stored photo exchanges, real time photo exchange, stored videoexchange and real time video.

The classification module, 150, comprises hardware and or software thattransmits and receives feature data, and that operates on feature datawhich classifies the agents relative to the feature data.

The classification information which may be exchanged between agents isin addition to the inter-agent communication described herein.

In one embodiment, the classification operates on feature data from anagent of the proximate group, generally the parent agent, whichclassifies the associated neighbor agent relative to the feature data.

In one embodiment, the classification operates on directed inputs fromone or more participants in the associated neighbor group(auto-classification).

In one embodiment, the classification operates on directed inputs ordata mined from about one or more sources that are not in the associatedneighbor group (external auto-classification). For example, the agentsmay be classified as members of an organization, e.g., The Sierra Club.

The classification module may consist of classification operationsexecuted on a distributed platform. Such computations may be performedin residence on the agent, or in part or in whole utilizingcomputational resources external to the agent.

Depending on the requirements of each particular implementation, theclassification module may consist of combinations of the classificationmodule examples described above.

The classification operation may be one that provides results which mayare qualitative.

The classification operation may be one that provides results which mayare binary.

The classification operation may be one that provides results which mayare variable.

The classification operation may be one that provides results which area derived types with any combination of types described herein.

The classification operation may be dynamically driven via inter-agentcommunication. For example, a member of an active proximate subgroup ata lecture may send out a classification query, e.g., “likes speaker.”The complementary members of the proximate subgroup may activelyauto-classify into the binary groupings, e.g., “like the currentspeaker,” or “dislike the current speaker.”

The classification may be parametrically stored in the agent structure.The information may be transient and stored only during the period ofparticipation in the particular associated proximate group.

In one implementation, the classification query, for selected layers, isinitiated by the parent agent. The feature data is transmitted to theassociated proximate subgroup neighbors, and the classification resultsare obtained for each member of the proximate group. The classificationresults are transmitted to the parent agent.

This classification is shown in FIG. 8, which shows the subgroup ofelements in a binary classification operation. A classification query,500, from the parent agent, 205, to elements within the discriminationzone, 250, which activates the classification process on each associatedneighbor agent. In FIG. 8, the binary classification results aredepicted schematically as classified neighbor agents with either a solidborder, 210, or with a dashed border, 510. The classification resultsare transmitted, 500, back to the parent agent, 205.

Agents, 255, outside of the discrimination zone are excluded from theclassification operation.

The data exchange and classification may not provide notification to theassociated neighbor agent that the classification occurs (one-wayanonymous classification).

The data exchange and classification in may also include relativepositional data, and possible additional neighbor agent data (one-waynon-anonymous notification).

The data exchange and classification may include notification to theneighbor agent that the classification occurs (anonymousclassification).

The communication to the neighbor agent may also include relativepositional data, and possible additional neighbor agent data(non-anonymous classification).

The data exchange and classification structure may be configured suchthat combinations of one-way, two-way, anonymous, and non-anonymouscommunication and exchange are used within an agent group.

In certain embodiments, neighbor member agents may initiate aclassification query within the active existing proximate group, andreceive classification data, as acting parent agent.

FIG. 9 depicts an exemplary structure of a prototypical agent, 550,including the communication/notification interface, 555, and three datalayers, 561, 571, and 581. Each of the three layers has a feature datacomponent, 562, 572, and 582, respectively, and corresponding computermachine components, 563, 573, and 583, respectively.

The executive information principal to each data layer contains thelayer name, and status, i.e.; active/inactive.

Note that although the data layers are shown in the figure, the numberof data layers may vary by agent, and by implementation.

The communication/notification interface, 555, may send and receive datafrom associated agents, from external entities, and from direct userinput/output.

Communication to and from other agents may be in the form of wirelessnetwork transmissions, or hardwired network transmissions.

Communication to and from other agents may be in the form of wireless,or hardwired signals independent of any internet, or intranet.

Communication to and from other agents may by way of audible orinaudible sound or vibration or other form of transducer interaction.

Communication to and from other agents may by way of infrared, or anyother band of EMF.

The communication/notification interface to the user may consist of anysuitable combination of audible, sensory, text, touch, and graphicalinput and display used to facilitate the implementations describedherein.

In one exemplary implementation, the interface consists of a graphicalmap display which shows the relative positional data, as available, ofagents in the proximate group.

Classification and notification data may be displayed as coloration,text tagging, or other visual indications associated with proximateagents.

Communications and data may be associated and accessible with userinteraction, For example, classification results may be displayed in adrop down layer type display structure.

Different communication and notification interface implementations maybe used for different aspects of inter-agent classification andcommunication.

Combinations of different notification and input modes may be used.

Communication and notification may be implemented on any suitableelectronic device. Note that the communication and notification may beexchanged using a device other than the agent to or from which saidcommunication is directed. For example, a laptop computer may be used toview a map like environment representing the proximate group of agentsas displayed for one of the member agents, although said member agentmay be embodied by a user with a cell phone.

The layer structure, consisting of feature data and computer machine maybe used to develop classification data beyond communications establishedbetween agents in the proximate group.

The feature element of each layer has the data structure for theclassification, and contains the relevant data that may be necessary forthe classification.

The layer structure may be dynamic.

The computer machine has the construct necessary to classify the featuredata input from an agent.

The feature data component and computer machine component of eachclassification layer may be completely different, or may have the samestructure as for other layers.

The classification may be dynamic (performed in real time upon therequest query), or it may be stored as part of the agent structure.

In one implementation, the classification layer may consist of featuredata and computer machine to provide a binary classification, i.e.; andis or is not classification relative to a category per the feature data.

The binary classification may be a self-categorization (autoclassification), e.g.; male. The feature data set consists of the datadescriptor (which may even be the same as the layer name), and thecomputer machine consists of the categorization which may be setdynamically or may be pre-set.

In one implementation, the feature data will contain the data name, andthe categorization will simply have the result of theauto-categorization. An exemplary layer structure for a binaryfeature/classification layer is shown in FIG. 10, in which the datalayer, 600, shows executive structure, i.e.; layer name and status. Thefeature data component, 605, contains a binary classification element,“Republican”. The computer machine component, 610, holds the binaryclassification switch for each corresponding element of the featuredata.

The computer machine may be simply an external categorization, byanother agent or organization. For example, the layer may be Sierra Clubmembership, and the feature data consists of personal identityinformation, and the classification (is a member, or is not a member) isprovided to the layer by the Sierra Club.

In an additional implementation, the classification layer may consist offeature data and computer machine to provide a multiplex classification,of several possible variables. Such a layer is similar to the constructof multiple binary layers. An exemplary structure is shown in FIG. 11,showing a multiple binary classification layer, 615. The feature datacomponent, 620, contains three binary classification elements,“Republican”, “Democrat”, and “Independent”. The computer machinecomponent, 625, holds the binary classification switches for eachcorresponding element of the feature data.

In another implementation, the classification layer may consist offeature data and computer machine to provide a multivariateclassification. Such a layer may classify in a manner that is notbinary, but rather has multiple possible answers. An exemplary structureis shown in FIG. 12, showing a multivariate classification data layer,630. The feature data component, 635, and computer machine component,640, are structured appropriately for a classification that admits somerange of possible answers.

The multivariate example, “Political Affiliation”, shown In FIG. 12admits a discrete multivariate classification, i.e.; “Republican”, or“Democrat”, or “Independent”, etc. It is also possible to have acontinuous multivariate classification, for example, “Height”, in whichcase the possible classification is number from a continuum.

The classification structures may consist of auto-categorizations, orexternally derived classifications.

In one embodiment, the classification is performed by the computermachine using data mining techniques.

In one embodiment, the computer machine may consist of statisticallyderived group preferences using agent data,

Feature data may consist of sufficient agent (personal) information fromwhich the computer machine may make data mining inquiries over externaldata bases. The computer machine may extract data from internet sources,social networks, and other social media, e.g., Twitter, from which thecomputer machine may classify the agent.

Feature data in the form of other classifications or categorizations maybe used by the computer machine to classify using data miningtechniques. For example, a computer machine may suggest that there is astrong correlation between National Rifle Association membership andRepublican political registration. Thus, if the feature data from anagents contains the classification “National Rifle Association”, thenthe computer machine may thus classify the agent as “Republican”.

The feature data may serve as input to a computer machine that isstatistically constructed. For example, the computer machine for anassociated neighbor agent may contain the statistical construct that theagent has a preference for blond women between 20-25 years old. Thatassociated agent would thus be classified as attracted to a parent agentthat is a 22-year old blond.

Combinations of said feature data and classification operations may beused.

Feature data may consist of descriptive elements that are specificallytailored to special classification computer machines. For example, anattraction classification may use feature data that is abstract, such aseigenvectors from image principle component analysis, or other imageprocessing feature extractions.

The computer machine to operate on such abstract feature extractions mayalso rely on training and parameterization using abstract feature datasets.

The agent structure and classification mechanisms described permitadditional implementations somewhat outside the notion of communicationand classification within the proximate agent group.

Entities mentioned herein may be people, corporations, groups, products,institutions, etc.

Such an exemplary implementation is one in which entities rather thanparticipating agents are classified for the parent agent. For examplenatural language processing may be utilized to perform sentimentanalysis on a collection of publications to determine the politicaldisposition of a newspaper author.

In such an exemplary implementation, news articles may be taggedfollowing the inclinations (as derived by the analysis) of the author.

Other data mining and analysis methodologies may be implemented. Forexample, correlative models may associate membership in some specificgroup with a certain political affiliation.

Such data mining and analysis models may be implemented on the systemlevel, or may be implemented at the agent level. Such models may beindividually derived, or may be made available by the systeminstitution.

Other similar versions of such implementations are possible using datamining and natural language processing analysis directed at entities tobe classified.

Agent input may also be used to verify or refute such classifications.Data obtained from such processes provide relative classificationplacements.

Such models described herein, embodied in the computer machine elementwithin an agent, may be stored, shared, exchanged, created, and modifiedas utility objects.

Such classification may consist of classification operations executed ona distributed platform, on the agent level, or in part or in wholeutilizing computational resources external to the agent.

The body of agents for the implementations described herein is createdthrough an initialization process shown in FIG. 13.

A potential agent, 650, downloads the application software, 660, from acontent server, 655. The potential agent with the software, 665, thenregisters, 675, with the software registration server, 670, whereuponthe agent becomes a member agent, 680, and is a member of the collectiveuniverse of member agents, 215.

The collective universe of agents, 215, is the group from which allparent agents and proximate groups may be drawn.

It is possible for an agent to reside in a refined sub-universe ofagents. Said refinement is a semi-permanent step of groupdiscrimination, as described above.

Note that the content server, 655, and the registration server, 670, areprocess entities, which may be embodied on the same device, or onmultiple devices.

The download, 660, and registration, 675, processes may be accomplishedin one step or multiple steps.

The potential agents consist of, but are not limited to, users with cellphones, tablets, desktops, and shared environment computational devices.

It may be possible for an agent to have more than one member presence ona single device, i.e., different user profiles.

FIG. 14 schematically depicts the process of forming a proximate agentsubgroup. The parent agent, 205, initiates a discrimination request,705, which is transmitted to the network agent, 700.

The discrimination request may contain any combination of geographiclocation data, and discrimination parameters. The network agent, 700,may forward the discrimination request to other network agents, 701,702.

One or more of the network agents may then initiate communication, 720,to and or from the group of agents covered by the network agents. Thelocation parameters, and or any additional discrimination parameters arethen used to distinguish the appropriate proximate neighbor subgroupagents, 210.

It certain implementations, it may be possible for agents in theuniverse group to utilize preference filters to prevent theirincorporation into certain parent agent proximate groupings.

In certain embodiments, preference filters on the proximate grouprestrict inclusion to certain features. Said preference filters may beprovided by the parent agent. Said preference filters may be implementedat the system level.

Agents, 255, that do not meet the proximity classification requirementsand/or preference filters are excluded from the associated neighborgroup.

In one exemplary embodiment, an existing proximate group will have anassociated log-on key so that agents may be joined in to the proximategroup following the structure in FIG. 4.

Such log-on key usage may be open (all agents), or limited (someagents), or restricted (parent agent) only. Such log-on usage may bedetermined at the system level. Combinations of the usage structure maybe used.

In certain implementations, it may be possible for agents that have beendiscriminated into a proximate group to sever ties with (to leave) theproximate group.

The neighbor agent subgroup(s) and communication links thus establishedare then used, in part, for subsequent communication and classificationsas shown in FIG. 7 and FIG. 8.

The proximate group associations and communications channels may bestored for semi-permanent use. In such instances, the associates aremaintained even if a subsequent discrimination request, using the exactsame parameters, would not result in the same proximate group.

The proximate group associations may be programmed to terminate in aspecified time, or may be terminated instantaneously by the parentagent.

In one implementation, and agent may be removed from the proximate groupfollowing a period of inactivity.

The temporal nature of the agent membership in the proximate neighborgroup is established by combinations of preference settings of theparent agent, the neighbor agent, and the system parameters.

The settings established during the initialization process determine thecommunications that transpire during the discrimination operation.

In certain implementations, the communication settings may be adjustedby some or all of the user agents.

The communication settings may be different for different agents in theproximate group.

Upon discrimination, the presence of an agent in the proximate group maybe transmitted back to the parent agent.

Upon discrimination, the presence and any combination of location andother identifying features may be transmitted from one or more of theneighbor agent to the parent agent.

Open discrimination, notification of the discrimination operation may betransmitted to one or more potential neighbor agents.

Open discrimination, notification of the discrimination operation anycombination of location and other identifying features may betransmitted from the parent agent to one or more potential neighboragents.

The transmitted information to/from agents may be representative in thatthe information may be stored on one or more network entities andtransmitted to or from said network entity.

In one implementation, the discrimination operation is reinitiatedperiodically, as the parent agent location moves.

In another implementation, the discrimination operation is periodicallyreinitiated as the proximate zone following the moving path of theparameterized discrimination target.

In one implementation, the structure of the communication (restrictedcommunication, open communication, structured communication, and freecommunication) is established at the outset by the parent agent, as partof the permissions in the query.

In one implementation, the structure of the communication (restrictedcommunication, open communication, structured communication, and freecommunication) is established at the system level.

Combinations of the communication structuring process may be used,depending on the requirements of each particular implementation.

The layer structure may be initialized at a system level, or may be setup by the user during the initialization stage, or may be initiallytailored by implementation at inception.

In one implementation, the layer structure may be altered using a listof layers available at the system level. In such an implementation,layers may be initialized (populated with relevant data by the user).

In one implementation, layers may be created and added by user agents.

It may be possible to send or receive layer structures, as in the formof an invite.

Layer structures may be added, as in layer allowances, using a trademetric.

Layers may be active or inactive and have permission preferences. Forexample, an agent may have initialized the auto-classification layers.“Republican”, and “Sierra Club”. Said agent may have the layer“Republican” active and the layer “Sierra Club” inactive. In such animplementation, for example, a parent agent query would reveal theagent's “Republican” classification, but would not reveal the agent's“Sierra Club” classification.

Different levels of information transmitted, or restricted based onagent layer user preferences. For example, that the agent has a “SierraClub” layer at all may be transmitted, but not the relatedclassification. As another example, the agent may appear invisible toother agents viewing by layer, and choosing to view the “Sierra Club”.

In one implementation, permissions and preferences on layers may bedetermined at an agent level and controlled by the agent.

In another implementation, permissions and preferences are controlled ata system level.

Layer permissions and preferences may be established on a layer by layerbasis.

Combinations of the approaches to layer permissions and usage arepossible.

Several exemplary embodiments are now described to illustrate thepotential utilities available within the present structure. The examplesare in no way restrictive. They are constructions that demonstrate avariety of implementations.

In one exemplary implementation, the structure described herein isutilized to create a local communication group. A query is initiated todevelop a proximate group of agents, amongst which communication mayoccur. Following the process shown in FIG. 14, for example, a proximategroup of agents is established. The group, exemplified as shown in FIG.7, then has an established communication link motivated by certaincommonality, i.e., all at the same bar.

The discrimination operation is periodically performed to establish andrefine the group of agents that are presently within the proximateregion. In this manner, new agents within the locality are added to theproximate group and agents that have left the locality are removed fromthe proximate group.

In one exemplary implementation, potential member agents have theiragent presence set up so that any proximity query results innotification of the member agent's classification status upondiscrimination. The structure results in a form of a digital bumpersticker in which agents prominently display certain traits upondiscrimination. For example, the agents may have the binaryclassification layer “Republican” with the computer machine set as“yes”, reference FIG. 10.

During a query by any agent, the discrimination communication from theneighbor agent back to the parent agent, shown as 3046 in FIG. 14,contains trigger information to automatically notify the parent agentregarding the classification status of the neighbor agent.

The notification is independent of any direct communication, orsubsequent classification queries. In essence, said classificationinformation is transmitted at first contact, thus forcing theclassification to prominence.

Such classification notification is the first part of any communicationto the parent agent or to other member agents.

An extension of the implementation may incorporate notification for morethan one classification layer.

An extension of the implementation may incorporate notification for morethan one classification layer

In one exemplary implementation, a parent agent initiates a selectivesearch in which a refined proximate group is determined containing onlythose neighbor agents that have a particular layer and classificationstatus. For example, the parent agent query for “Republican” agents willindicate the existence of proximate agents that have the layer“Republican” and an auto classification “yes”.

Such an implementation may proceed as shown in FIG. 14, in which theselective layer status is an additional parameter utilized within theoperations performed by network agent, 3040, to further discriminate theproximate group to satisfy the selective classification request, i.e.,“Republican”.

Such an implementation may also proceed by first performing a proximityquery without the aforementioned layer selectivity. The base proximategroup is then divided by a network agent into a selective discriminantsubgroup, per the initial request, and a complementary proximatesubgroup of agents that do not meet the selective search criterion ofthe initial search.

In another exemplary implantation a parent agent may initiate aselective search in which a refined proximate group is determinedcontaining only those neighbor agents that do not have a particularlayer and classification status.

The implementation may be extended to selective searches that are forcombinations of attributes within the proximate subgroup.

In one exemplary implementation using the structure described herein, aparent agent initiates a query to develop a proximate subgroup fromwhich classification information is gleaned. As one particular example,an attraction layer is utilized so that the parent agent may determineif there are agents in the proximate group which are likely to find theparent agent attractive. The situation is as depicted in FIG. 8

The process is proceeds as depicted in FIG. 14, in which a parent agentquery discriminates neighbor agents. The neighbor agents in theproximate group might be, for example, all participating agents with 100feet of a certain locale.

Upon discrimination, the neighbor agents are classified as shown in FIG.15, which depicts the classification interaction between the parentagent, 205, and an associated neighbor agent, 210. The parent agent isshown with one active layer, 750, and two inactive layers 765. Theassociated neighbor agent is shown with one active layer, 775, and twoinactive layers, 765.

The parent agent feature data, 755, from the active layer, 750, istransmitted, 770, to the computer machine, 785, of the associated activelayer, 775, of the associated neighbor agent, 210.

The computer machine, 785, on the associated neighbor agent activelayer, 775 operates on the feature data to create a classificationresult which is transmitted, 790, back to the communication/notificationinterface, 555, of the parent agent. The feature data, 780, of theassociated neighbor agent, may or may not be utilized in theclassification process.

The classification may involve notification and communication throughthe associated agent communication/notification module, 555.

The process depicted in FIG. 15 is repeated for each of the associatedneighbor agents in the proximate group.

Using the aforementioned example of attraction, the feature data of theparent agent may consist of demographic data, descriptive data, andstatistical data that have been previously generated as part of thetraining process for the attraction metric.

The computer machine in the corresponding layer of each associatedneighbor agent may consist of a computer construct generated tostatistically capture the neighbor agent's attraction preferences. Theconstruct has been previously generated as part of the training processfor the attraction metric.

Additional attraction metric structures outside of the present exampleare available.

The classification process, using the computer machine, 785, issymbolically contained in the associated neighbor agent, 210. Theclassification may be performed in part or in whole on an externaldevice.

FIG. 16 depicts an agent, 800, with one active layer, 775, and twoinactive layers, 765, utilizing external computing resource to process aclassification operation for the active layer. Relevant data, i.e.,feature data from the parent agent, is transmitted, 810, from the agentcomputer machine, 785, to a network agent, 700, that providescomputational resource to perform the classification operation.

Some classification operations utilize externally provided data. Suchexternally available data may obtain through a query to an externalsource. The request is transmitted, 810, from the computer machine, 785,to the network agent, 700, and then transmitted, 815, to the networkresource server, 805, The data from the external source is downloaded,820, to the network agent, 700, for processing. The data and is thentransmitted, 825, to the agent computer machine, 785. Additional cyclesof the data search and retrieval may be performed as part of theclassification operation.

External data and external computer processing may be performed in anycombination by the network agent, 700, and network resource server, 805.Processing may also be performed in whole or in part in combination bythe computer machine, 785, and the external resources.

Feature data, 780, from the agent active layer, may or may not beutilized in the classification process.

Notification to the parent agent may use any combination of the agentinteraction types, anonymous, non-anonymous, one-way, and two-way.

In one exemplary implementation, the agent structure and classificationmechanisms described herein are used to create a mechanism by which thepolitical predisposition of manuscript authors are determined and usedto tag (classify) the associated work. The process is different fromother example implementations described herein in that theclassification operation is not performed on an agent

The classification uses the same process as shown in FIG. 16, where thename of the author of the article is supplied to the computer machine,785, by the agent communication/notification interface, 555. Thecomputer machine, 785, transmits, 810, the author name to a networkagent, 700. A data query, 815 is made to network resource server, 805,where data mining techniques and natural language processing are used toassess the sentiment bias of the article author. The bias assessmentresults are transmitted back, 820, to the network agent, 800, and thenback, 825, the computer machine, 785. The final result is transmitted tothe notification interface, 555, where the article is tagged accordingto the author bias.

Classification results may be stored as multivariate classificationstructures, reference FIG. 12 to save on future computational overhead.The classification bias may be refreshed at any time, or may beperformed every time.

Numerous data mining and classification operations are available usingexisting art, for example, natural language processing and sentimentanalysis, or a statistical preference model following a market basketanalysis.

The present structures and processes permit the implementation of peerto peer data exchange with dynamic demand.

In one exemplary embodiment, a peer to peer exchange for real time videowith proximity driven dynamic demand.

The process is depicted in FIG. 17, which shows a parent agent, 205,which queries for video at the target location, 850. Informationconcerning the request is transmitted, 870, to an adjacent networkagent, 700, and then to another network agent, 701, adjacent to thetarget location. A zone of proximity, 860, is established and scanned,880, for candidate source agents therein.

Should no appropriate agent exist in the zone of proximity, the zone ismonitored until a member agent which is a potential neighbor agent, 685,enters the zone and becomes neighbor agent, 210, which is a candidatesource agent.

The parent agent request is enunciated to candidate source agents.

When a candidate source agent accepts the parent agent request, thevideo is uploaded, 885, to the network agent, 701, and transmitted viathe agent network back to the parent agent.

The video thus sourced and transmitted may be archived as part of alibrary structure for future availability, or may be abandoned.

Data other than video may be exchanged in the same fashion. The intentis to provide dynamic demand for data of geographic and temporalimportance. For example, data may be requested, in real time on locationat breaking news story. As another example, real time video may berequested following a Rose Parade float.

Location categorized information about agents creates an environmentfrom which a game may be created. The present exemplary implementationpresumes that proximate group has been established at some location,i.e., a bar, and geographic position data of each proximate group memberagent has been openly broadcast. Additionally, it is presumed that oneor more agents have been classified, and the classification has been hasbeen enunciated openly to the group anonymously.

The example configuration thus described is one in which thecommunication/notification module of each element displays theapproximate location of each agent in the proximate group. Theclassification data, not being linked to any geographic data, is notattached to any particular one of the proximate agents.

The exemplary game challenge is for an agent to guess, based uponappearance, for instance, which of the proximate agents fit theavailable classification data. The guess can be directed to a particularagent using the positional aspect of the communication/notificationinterface.

The guess can be verified as correct or incorrect using the availablesystem agent data and the result can be communicated to the agent makingthe guess.

Variations of the game are possible. As another example, a proximateagent's classification with regard to some layer, i.e., “Republican” ismade. The guess is tested against system agent information, and theresult is transmitted back to the agent making the guess.

Certain embodiments described herein may generally be described throughthe process diagram shown in FIG. 18.

The proximity query, 1800, is the operation by which the parent agentinitiates the formation of a proximate group. The parent agent uses thecommunication/notification interface, 555, to select and enter proximityparameters, and communication and notification preferences. Theclassification option is specified and active layers for classificationare selected. The proximity query parameters, communication/notificationpreferences, and classification options, are entered into the parentagent communication/notification interface to form a data input set thatused as input in subsequent operations. The communication/notificationinterface is used to commence the discrimination operation, 1805.

When the discrimination option is commenced, 1805, the proximity queryparameters, communication/notification preferences, and classificationoptions that had been entered into the parent agentcommunication/notification interface are uploaded to a network agent.The network agent uses the proximity parameters as input for thediscrimination operation. In certain implementations, other parts of thedata entered during the proximity query, 1800, are also used as input tothe discrimination operation, 1805. The network agent uses networkresources to determine which member agents, 685, satisfy the proximityparameters selected in 800. The network identity and additional agentinformation relevant to the data input set entered in the proximityquery, 1800, are obtained and utilized in the subsequent processes shownin FIG. 18. This completes the discrimination operation, 805.

The notification process, 1810, provides announcements and indicationsto the agents concerning various aspects of the discrimination process,1805. The existences of each of the neighbor agents, 210, that aredetected and discriminated during the discrimination process, 1805, aretransmitted to the parent agent, 205, from the network agent, 700.Depending upon the communication/notification preferences, geographicinformation and/or additional neighbor agent, 210, data are alsotransmitted from the network agent, 700, to the parent agent. Dependingupon the communication/notification preferences, the member agents, 685,that are queried as part of the discrimination process are notified ofthe discrimination process. Depending upon thecommunication/notification preferences, the neighbor agents, 210, thatare established as part of the discrimination process are notified oftheir inclusion in the proximate group, and possibly geographicinformation and/or additional information about the parent agent. Thenotification process communicates the proximate group structure to theparent agent, 205, and neighbor agents, 210, and indicates theclassification, 1820, and/or communication and data exchange, 1830,operations to follow.

The communication/classification decision point, 1815, shows the processbranch, from which, the classification and communication operationsbegin. If neighbor agent classification has been requested as part ofthe query, 1800, then the parent agent, 205, communication/notificationinterface, 555, provides a prompt for the classification operation,1820, to commence, and also for communication and data exchange amongstagents, 1830, to commence. Each of the operations, classification, 1820,and communication, 1830, commences upon execution by the parent agent,205, at the decision point, 1815. The classification operation, 1820,and communication operation, 1830, may be performed in either order.Additional cycles of each may be performed, as shown on FIG. 18. Ifneither operation can be performed, the process stops, 1835. Neitheroperation can be performed when the proximate group is vacated, orexpires, or is terminated by the parent agent or network agent.

Upon commencement of the classification operation, 1820, from thedecision point, 1815, the classification operation, 1820, evaluatesneighbor agents, 210, relative to feature data from parent agent, 755,or relative to other data independent of parent agent feature data, 755.Using the communication links established as part of the discriminationoperation, 1805, the selected classification layer data are transmittedfrom the parent agent, 205, to the network agent, 700, and then to theneighbor agents, 210. The neighbor agents, 210 operate on the featuredata, 755, and are classified accordingly. The classification resultsare transmitted to the network agent, 700, and then to the parent agent,205, where the results are displayed on the communication/notificationinterface, 555. Depending upon the communication/notificationpreferences, the neighbor agents, 210, may or may not be notified of theclassification process. Subsequent classifications may be initiatedwithin the same process block, 1820, when the parent agent, 205, selectsa different data layer for classification, and the classificationprocess just described is repeated. Each classification operation iscompleted when the neighbor agents, 210, are classified, and theclassification process results have been transmitted to the agents. Fromthe classification process block, 1820, control returns to the decisionpoint, 1815.

The communication process, 1830, provides notifications and dataexchange between agents in the proximate group, 225. Using thecommunication links established as part of the discrimination operation,1805, agents within the proximate group communicate and transmit datawithin the confines of the permissions and preferences establishedduring the discrimination process, 1805. The communication and dataexchange is performed (or initiated) using the agentcommunication/notification interface, 555. The communication process,1830, is completed when the message, email, text, video, or data file toor from the agent has been transmitted and received, or the process hasbeen interrupted by the parent agent, 205, neighbor agent, 210, ornetwork agent, 700. From the communication block, 1830, control returnsto the decision point, 1815.

Additional process structures are possible, describing implementationvariations permitted by processes and structures described herein.

The process depicted on FIG. 18 is shown in greater detail in FIGS. 19A,19B, and 19C.

The parent agent proximity parameters, 1900, are the operation by whichthe parent agent initiates the formation of a proximate group. Theparent agent uses the communication/notification interface to select andenter proximity parameters, and communication and notificationpreferences. The classification option is specified and active layersfor classification are selected. Such parameters and such preferencesmay be stored as profile data and selected for subsequent queries.

Proximity parameters include geometric proximity parameters, and alsoadditional parametric proximity parameters. Geometric proximityparameters include location data, i.e.; centroid and radius of theproximity zone. Additional proximity parameters include parametric dataas permissible age, or sex. Such parameters are part of each agent'sinformation profile and/or part of (or not) an agent's layer datastructure

Communication preferences indicate the level of notification andcommunication to be provided to and from agents during thediscrimination and classification options, such as one-way non-anonymouscommunication in which the parent agent is to be notified of theexistence of agents in the proximate region, and also of the location ofeach agent in the proximate region.

The classification option indicates whether or not any classificationoperation will be performed on the agents in the proximate region. Theoption to classify and to also establish a communication link is alsoprovided.

Additional temporal and geographical specifications, such as theduration of the proximate grouping or multiple proximity regionlocations are also indicated.

The communication/notification interface is used to commence the query,upon which the proximity query data is uploaded to a network agent,1905. The data entered to establish the proximate zone discriminationand other communication preferences and permissions, are transmitted toa network agent, 700, in preparation for the subsequent operations.

The network agent, 700, transmits the proximity parameters, asappropriate to other network agents, 700, to facilitate agent detection,1910. Member agents, 685, that are within the geometric limits of thediscrimination zone, 250, are detected and identified, 1910, by networkagents, 700. The network identity and agent data for agents within thegeometric limits of the proximity zone are used in the subsequentdiscrimination operation, 1915.

The discrimination operation is shown in 1915. Using the agent dataobtained in 1910, the additional proximity parameters, beyond geometricproximity, are applied to each of the agents detected in 1910, to refinethe group of agents. Agents that satisfy the geometric proximityparameters and that also satisfy the additional proximity parameters arecategorized as neighbor agents, 210. Agents that do not satisfy all ofthe proximity parameters are categorized as excluded agents, 255. Thediscrimination operation, 1915, stops when all of the agents detected in1910 have been adjudicated relative to all additional proximityparameters. The network identities, agent data, and categorization(neighbor agent, 210, or excluded agent, 255) are utilized as part ofthe input for the notification process, 1920.

The parent agent, 205, is notified regarding the process and results ofthe detection, 1910, and discrimination, 1915, operations. The existenceof each of the neighbor agents, 210, that are detected, 1910, anddiscriminated, 1920, are transmitted to the parent agent, 205, from thenetwork agent, 700. Depending upon the communication/notificationpreferences, geographic information and/or additional neighbor agent,210, data are also transmitted from the network agent, 700, to theparent agent. The notifications are utilized as input for the subsequentparent agent, 205, decision point, 1925.

The notifications provided in 1920 are displayed on thecommunication/notification interface, 555, and reviewed by the parentagent, 205, from which a decision, 1925, to revise the proximityparameters is made. If the resultant proximate group of agents, 225, isnot satisfactory, the proximity parameters may be revised, to restartthe process, 1900. A prompt on the communication/notification interfaceis provided to either revise the proximate group, 225, or to accept theproximate group, 225. If the resultant proximate group of agents, 225,is satisfactory, the neighbor agent data and proximity query preferencesare utilized as input for the neighbor agent notification decisionpoint, 1930.

The decision to notify neighbor agents, or not, is shown in 1930. Theneighbor agents, 210, are notified of their inclusion in the proximategroup, 225, if the proximity query preferences input in 1900 indicatenotification to neighbor agents. If the notification is indicated, amessage is sent from the network agent, 700, to each of the neighboragents notifying them of their inclusion into a proximate neighborgroup. Depending upon the preferences set forth as part whenestablishing the discrimination parameters, geographical and/or otherdata about the parent agent, 205, is transmitted from the network agent,700, to the neighbor agents, 210. Following notification, the processcontrol flows to the decision point in which the parent chooses, 1940,to classify the neighbor agents, or communicate with the group ofneighbor agents.

The communication/classification decision point, 1940, shows the processbranch, from which, the classification and communication operationsbegin. If neighbor agent classification has been requested as part ofthe query parameters, 1900, then the parent agent, 205,communication/notification interface, 555, provides a prompt for theclassification operations to commence, and also for communication anddata exchange amongst agents, 1945, to commence. Each of the operations,classification, and communication commences upon execution by the parentagent, 205, at the decision point, 1940. The classification operations,and communication operations may be performed in either order.Additional cycles of each may be performed, as shown on FIG. 19. Ifneither operation can be performed, the process stops, 1980. Neitheroperation can be performed when the proximate group is vacated, orexpires, or is terminated by the parent agent or network agent.

If classification is chosen at the decision point, 1940, the processcontrol flows to process block 1950, where the parent agent, 205,information is transmitted, 1950, to each neighbor agent, 210. Thefeature data, 755, from the parent agent, 205 is transmitted to anetwork agent, 700, and then to the computer machine, 785, of eachneighbor agent, 210. The feature data is used as input for thesubsequent classification, 1955. The data transfer, 1950, is completewhen the feature data has been transmitted and received by each of theneighbor agents.

Process control transfers to the classification operation, 1950, thecomputer machine, 785, of each neighbor agent, 210, operates on theparent agent, 205, feature data, 755, to obtain a classification result.The process is complete when each of the neighbor agent's classificationis established. Process control then flows to process block 1960 wherethe classification results are communicated.

The classification result of each neighbor agent is transmitted to anetwork agent, 700, from when it is transmitted to thecommunication/notification interface, 555, of the parent agent, 205.Depending upon the preferences established by the initial proximityparameters used to initiate the formation of the proximate group, 1900,the notification may or may not be provided to neighbor agents that theyhave been classified. The classification results are viewed on theparent agent, 205, communication/notification interface, 555 Titheclassification transmission, 1960, is complete when all of the neighborclassification results have been transmitted to the parent agent, 205.The process then returns to the classify or communicate decision point,1940.

If communication is chosen at the classify or communicate decisionpoint, 1940, the process control flows to process block 1945, tocommence inter-agent communication amongst agents in the proximategroup, 225. The communication process, 1945, provides notifications anddata exchange between agents in the proximate group, 225. Using thecommunication links established as part of the discrimination operation,1915, agents within the proximate group, 225, communicate and transmitdata within the confines of the permissions and preferences that areestablished during formation of the proximate group, 225. Thecommunication and data exchange is performed (or initiated) using theagent communication/notification interface, 555. The communicationprocess, 1945, is completed when the message, email, text, video, ordata file to or from the agent has been transmitted and received, or theprocess has been interrupted by the parent agent, 205, neighbor agent,210, or network agent, 700. From the communication block, 1945, controlreturns to the classify or communicate decision point, 1940.

Embodiments are not limited to processes described in FIGS. 18, 19A,19B, and 19C.

According to exemplary embodiments of the present invention, three novelimplementations of facial recognition and data management technology forsocial applications are presented; a facial rolodex, a reverse facialrolodex, and a set social networking applications. Each of the threeimplementations may be utilized independently for use on a computer ormobile device, for example.

The third implementation (social networking applications) requires ametric (or combination of metrics) according to certain embodiments. Aparticularly suitable metric may be derived from FRT. The thirdimplementation is described independently from the choice of metric (toadmit application with any suitable metrics). The novel metric derivedfrom FRT is subsequently described.

The facial rolodex application is a facial data management application,that may or may not be facilitated using FRT. The concept is to have anapplication in which a name may be associated with a face in anorganized fashion. By using a linked face-name database, the user maycall up a picture of a name that is either typed, spoken using suitablevoice recognition technology (VRT), or in the text of an existingdocument. The essence of this application is to simply place a face witha name. The following features or design elements are presented:

Facial look-up. The application retrieves an associated facial image bytyping a name, or by speaking a name, or by scrolling through a list ordocument to find a name.

Tagged text in a document. The application presents a face imagecorresponding to a name in a text. The image is either being permanentor transient, and may be toggled on or off. The face image retrieval mayeither be passive (as when the cursor or selector icon is floated abovethe text) or active (as when the name text is highlighted and akeystroke or button is used to activate the facial image retrieval). Theapplication may be embedded upon existing text, or may be applied overtext as it is typed (as when a face is called up simply by typing thename, as the name is typed).

Text tagging. The application is used to create a real time pop-up of anassociated facial image for text messages or emails received.

Conversation tagging. The application is used to retrieve an associatedfacial image (and biographical information) for the names of people thatare associated to snips of conversation. This application may be usedfor ambient conversations, or for telephone or radio conversations. Itassociates a face with the speaker. For example, during conferencecalls, the application uses VRT to identify the person speaking, andretrieves the associated facial image (if the speaker is in the databaselibrary).

Conversation Capture. The application identifies names that were spokenduring conversations and retrieve associated images for the spokennames. The kernel of the application functions just as does conversationtagging, however the feature that triggers the data retrieval would be aspoken name.

Organizational Structure. The name-face database library may beaugmented by the user in the same fashion that contact information isadded and edited. The libraries may be shared or merged and may beorganized with associations, i.e.; a name-picture for your neighbor mayhave tree-like connection to the neighbor's wife, etc. Theorganizational structure may be presented in different fashions, as in atree diagram with connections (relationships) and nodes (entries).

Social Networking. The facial rolodex application and organizationalstructure may be implemented along with social media to allow a socialmedia presence to be linked to a face-name data pairing. The applicationmay be tasked with data mining the present webpage to create new, anddevelop existing, name-face-data data association's database.

Database building. Any element of the associations, name-face-data, foran entry in the database may be added to an existing entry, or enteredto create a new database entry. The database entries may be modified,completed, augmented or edited using additional information that becomesavailable through the processes to which the application is directed.

The reverse facial rolodex application is a facial data management toolthat uses FRT to identify faces and retrieve social contact and otherinformation attached to a face in the name-face library. It allows theuser to user to identify a facial image and allow a user to alsoretrieve data associated to a person. The essence is to simply place aname to a face. The following features or design elements are presented.

Direct image sensing. The application may be utilized to access archivalinformation linked to a facial image in the database that is enteredwith a cell phone. The cell phone may be used to scan a face or a crowdand thus retrieve information about any faces from the images that arein the facial image database.

Email image input. The application may be used to recall linkedinformation attached to a facial image in a picture embedded in orattached to an email.

Website image capture. The application may be used to retrieve linkedinformation associated with any facial image from the database that iscontained in a photograph that is attached to, linked to, or embedded ina webpage. The application may be tasked with drilling down and seekingfacial images at some level of association with the present webpage.

Document image capture. The application may be used to capture imagesembedded, linked, or tagged to a document and retrieve associatedinformation for any image in the database.

Video image capture. The application may be used to retrieve linkedinformation associated with any facial image from the database that iscontained in a video. Any of the resources, from which an image is madeavailable to the application may also be used to convey or provide videoto the application.

Social Networking. The reverse facial rolodex application andorganizational structure may be implemented along with social media toallow a social media presence to be linked to a name-face data pairing.The application may be tasked with data mining the present webpage todevelop and populate associated data linked to an image in the database,and to develop new name-face-data associations to add to the database.

Database building. Any element of the associations, name-face-data, foran entry in the database may be added to an existing entry, or enteredto create a new database entry. The database entries may be modified,completed, augmented or edited using additional information that becomesavailable through the processes to which the application is directed.

Methods have been developed for facial recognition using Eigen spaceanalysis. The Eigen face approach, casts the identification andclassification of facial images as a mathematical problem, utilizingvector space analysis, as used in numerous fields of science, math andengineering.

Most young people (in particular) are concerned with theirattractiveness to other people. When any individual describesattractiveness (how attractive someone is), there is some continuum;extremely attractive, very attractive, somewhat attractive, somewhatunattractive, etc. Thus, a metric of attraction can be described, andthe metric may be used in a social application.

Since the Eigen face concept codifies a facial image as an element of alarge dimensional vector space, which is axiomatically also a metricspace (with a Euclidean metric), it provides a mathematical structure bywhich attractiveness may quantified. This notion permits the developmentof the broader idea of a social application that addresses the issue ofattractiveness according to certain embodiments.

The following discussion describes applications according to certainembodiments, which ultimately may be used with a variety of metrics, orcombinations of metrics. Following the discussion of severalapplications, a specific metric obtained by using facial recognitionEigen space is described in detail according to certain embodiments. Anadditional suitable metric using neural networks is also described,according to other embodiments.

The application is addresses the issue of attractiveness, and theperception, by others, of one's attractiveness. A person may determinehow attractive he or she is to another individual without direct inputor interaction with that individual. Additionally, an individual may getsome measure of how attractive he or she would find another personwithout visually making that assessment. The ideas are extendedaccording to certain embodiments to situations that arise from theinformation that becomes available with a suitable attractivenessmetric.

If there exists a suitable metric of attractiveness, and if informationor parameters necessary to establish the metric which describes theirattraction to others is available, then certain measurements may beperformed. More precisely, different parties could make the measurementof attraction. For example:

An individual could measure how attractive any other person would be (Ameasures how attractive B is to A).

A person could measure how attractive an individual would find thatperson (B measures how attractive B is to A).

A third party measures attraction (C measures how attractive B is to Aand vice versa).

Provided that the individual's metric can be parameterized in somesufficiently convenient manner, then the metric may be reconstructed andthus applied as a test of any person's attractiveness. Thus, a virtualdevice is created that determines how attractive person B is to personA.

The application of the metric concept according to certain embodimentsmay be utilized for any social application website such as Facebook orInstagram. The utilization may take several forms, as listed below incertain exemplary embodiments:

Image Classification—(A measures) The user may actively or automaticallyclassify incoming images (or linked persons) by attractiveness to theuser. The resulting classification may simply be private, as aconvenience to the user. The resulting classification may also be madepublic (open to all), or semi-public (shared with chosen users).

Reverse Image Classification—(B measures) The user may measure howattractive he or she is to the person depicted in incoming images (orlinked persons). The resulting classification may be private (to B) orshared with the owner of the image (or linked person).

The application of the metric concept may be utilized as a searchengine, or to modify a search engine or search results. The utilizationmay be used in a dating website search, a social networking search, oron a generic search engine search.

Attraction Search—(A measures) A search may be made, using the metric,for people that are attractive to A.

Reverse Attraction Search—(B measures) A search may be made, using themetric, for people that would find An attractive.

Search Modification—A search may be made using traditional methods(keywords, AI, for instance) and modified using the metric. For example,search for brunettes, named Elizabeth, that find a certain personattractive.

The application of the metric concept could be used by a third party (Cmeasures) in fixed or mobile applications—(C measures). For example,person A may test to see if a person that person A meets at work wouldbe attractive to person A's neighbor. As a different example, person Amay test to see if a person A meets at work would find person A'sneighbor attractive.

The metric may be used as a stand-alone website or mobile application,in which users may test themselves by other users, and test other users.They may also search users for attractive or attracted users. The standalone may serve as a dating website, or simply as an interestingwaypoint, by which, members could judge their attractiveness.

The application of the metric concept may be utilized for a mobileapplication that utilizes position information to augment themeasurement available from the metric. The essence of the application isthat someone may see that there are people that find that personattractive in the same locality (at a party, for instance).

Blind Annunciation—The metric parameters of an individual's attractionmetric are broadcast locally according to certain embodiments. Themetric is then used by other users in the same locality to test theirown appearance. Thus, a person can see if there are other individuals inthe location that would find that person attractive. No specificinformation about either person or either person's precise location isexchanged, according to certain embodiments.

Open Annunciation—The metric parameters of an individual's attractionmetric are broadcast locally, according to certain embodiments. Themetric may then use by other users in the same locality to test theirown appearance. Thus, a person can see if there are other individuals inthe location that would find that person attractive. However, incontrast to the blind annunciation described above, some or allavailable information about one person, or the other (including,possibly, precise location) is transferred, according to certainembodiments.

The application is essentially the same as the mobile application incertain embodiments, except the information is integrated into a networkso that a user can view information about the people at a remotelocation, such as at a restaurant.

The application according to certain embodiments provides an objectivetest for attractiveness that is based upon a metric derived from a groupof people. The group metric may then be used to test the attractivenessof an individual to a group. This utilization may thus be used to see ifa particular individual is attractive to a group. This implementationmay be used for hiring and selection in professions where appearance isimportant.

By nature, the utilizations described herein present themselves togathering all manner of statistics regarding attractiveness. Thestatistics may be used to determine if a person is attractive to someparticular demographic. It may also be used to extract trends.

One particular formulation of FRT, Eigen faces, is cast in amathematical formulation that permits allows the development of anattractiveness metric that is particularly well suited for use in thesocial networking application described above. The Eigen face concepttreats facial recognition as a mathematical problem (eigen-analysis) inlarge scale, finite-dimensional vector space.

In certain embodiments, a photograph of a face is depicted as arectangular array of intensity values. It can thus be represented as avector of intensity values. For example, a 256×256 image can beexpressed as a vector in 65,536-dimensional space. Prior artists havenoted that a group of such images can be sufficiently well representedwhen the images are projected onto an appropriate subspace of theoriginal 65,536-dimensional space. The appropriate subspace isdetermined by performing an eigen-analysis on the covariance matrix ofthe group of images. Further, images that are not members of the samplegroup can be classified as facial images, or as images of something elseby determining the proximity of the tested image to the subspacecomprised of the sample images. The entire formulation is developed as afacial recognition technology that is effective and robust, thatrequires relatively low computer storage and relatively lowcomputational effort.

These formulations demonstrate the practical use of the mathematicalstructure (vector space) that is available when a facial image isdescribed as a finite dimensional vector. The present developmentaccording to certain embodiments (attractiveness metric) capitalizes onthose very same mathematical elements; finite dimensional vector space,subspace, eigen-analysis, norm, and metric. For sake of description, thefollowing discussion is presented in the context of 256×256 facial imagerepresentation. Note that the ideas may be directly extended to otherimage sizes, depending on the particular requirements of eachimplementation.

A finite dimensional real vector space has a Euclidean norm, and bysimple extension, has a metric (norm of the difference of two vectors).This is a familiar metric, however it must be noted that it is not theonly metric that can be constructed which will satisfy the definitionalproperty of a metric. Utilizing the subspace of images screened forattractiveness and/or the subspace of images screened forun-attractiveness presents an opportunity to utilize any such metricsavailable within the vector space construction, plus any combination ofmetrics and other features (brunette, Catholic, etc.).

A metric for attractiveness may be formed for an individual bypresenting the individual with a test set of 256×256 facial images whichthe individual categorizes as attractive, or not attractive. Theattractive images form a subspace in 256×256 face space. The attractionmetric is created by utilizing the vector space metric that is availableas part of the mathematical structure (vector space) utilized. Thedistance of the test image vector from the attractive image subspace isthe attraction metric; a very close image is deemed attractive, and avery distant image is deemed not attractive.

A metric for attractiveness may be formed for an individual bypresenting the individual with a test set of 256×256 facial images whichthe individual categorizes as attractive, or not attractive. Thenon-attractive images form a subspace in 256×256 face space. Theattraction metric is created by utilizing the vector space metric thatis available as part of the mathematical structure (vector space)utilized. The distance of the test image vector from the non-attractiveimage subspace is the attraction metric; a very close image is deemedattractive, and a very distant image is deemed not attractive.

A metric for attractiveness may be formed for an individual bypresenting the individual with a test set of 256×256 facial images whichthe individual categorizes as attractive, or not attractive. Theattractive images form a subspace in 256×256 face space and the imagesthat are non-attractive form the complementary subspace in 256×256 facespace. The attraction metric is created by utilizing the vector spacemetric that is available as part of the mathematical structure (vectorspace) utilized. The distance of the test image vector from theattractive image subspace and the distance of the of the test imagevector from the non-attractive image subspace provide two numbers usedto gauge the attractiveness of the test image.

A metric for attractiveness may be formed for an individual bypresenting the individual with a test set of 256×256 facial images towhich the individual assigns attraction values (1 to 10, for example).This scaling is simply used as a discrimination parameter for thedivision into attractive, and not-attractive subspaces used in themetrics described above.

A metric for attractiveness may be formed for an individual bypresenting the individual with a test set of 256×256 facial images towhich the individual assigns attraction values (1 to 10, for example).The test images form a subspace with an associated scaling (in essencean additional dimension—think of the three dimensional plot in which thetest vectors are in the X-Y plane, and the scaling is the associatedZ-value). The test image may be projected onto the sample space (closerto the attractive images, or closer to the un-attractive images) to seewhat the associated scale (attractiveness value) is for the projectedtest image.

Neural networks are a structural form of artificial intelligence thatare commonly applied to pattern recognition problems, such as facialrecognition technology. Neural networks are a kind of black box approachin which a network of simple behavioral cells (perceptron's, forinstance) are trained so that the response of the system adheressufficiently well to a desired algorithm.

In the same fashion that a neural network may be utilized to identify animage as a face, and select a particular face from a training set, aneural network may be trained to identify a face as attractive ornon-attractive according to an appropriate training set of images. Aneural network may therefore be used as attractiveness metric. Thenetwork, a back propagation, feed forward network, for example, wouldprovide discrete output signals such as attractive and non-attractive(two output cells), or attractive, non-attractive, neutrally attractive(three output cells), etc.

In this manner, a neural network may be trained to create attractivenessmetric for use in any of the aforementioned components of theattractiveness application.

There may be other combinations not explicitly presented here. Thereforeit is understood that the invention is not to be limited to the specificembodiments disclosed, and that modifications and embodiments areintended to be included as readily appreciated by those skilled in theart.

While the above description contains many specifics and certainexemplary embodiments have been described and shown in the accompanyingdrawings, it is to be understood that such embodiments are merelyillustrative of and not restrictive on the broad invention, and thatthis invention not be limited to the specific constructions andarrangements shown and described, since various other modifications mayoccur to those ordinarily skilled in the art, as mentioned above. Theinvention includes any combination or sub combination of the elementsfrom the different species and/or embodiments disclosed herein.

I claim:
 1. A method to establish a group of autonomous agents basedupon location, for establishing communication and notification ofclassifications amongst said agents comprising the steps of: defining aset of geometric parameters which describe a domain; detecting andidentifying a group of two or more agents as members of said domain andestablishing a communication link between said agents, wherein each suchagent is associated with a respective user computational device;notifying said agents of said detected and identified group;transmitting agent feature data associated with one or more of saidagents in said group to one or more of other said agents in said group,and classifying one or more agents in said group depending on saidtransmitted agent feature data, wherein said agent feature datacomprises attractiveness data associated with each of said one or moreof said agents in said group, and wherein a metric for saidattractiveness data may be formed for an individual by presenting saidindividual with a test set of facial images which the individualcategorizes as attractive or not attractive; and communicating saidclassifications to one or more of said agents in said group, whereinsaid step of notifying said agents of said detected and identified groupcomprises a notification of the presence of said one or more of saidagents within said domain.